论文标题
盲人下降：梯度下降的前传
Blind Descent: A Prequel to Gradient Descent
论文作者
论文摘要
我们描述了一种神经网络的替代学习方法，我们称之为盲人下降。根据设计，盲人下降不会面临诸如爆炸或消失梯度之类的问题。在盲目的下降中，梯度不用于指导学习过程。在本文中，与梯度下降相比，我们将盲人下降作为更基本的学习过程。我们还表明，梯度下降可以看作是盲人下降算法的特定情况。我们还使用最通用的盲人血统算法来展示两个神经网络架构，多层感知器和一个卷积神经网络。
We describe an alternative learning method for neural networks, which we call Blind Descent. By design, Blind Descent does not face problems like exploding or vanishing gradients. In Blind Descent, gradients are not used to guide the learning process. In this paper, we present Blind Descent as a more fundamental learning process compared to gradient descent. We also show that gradient descent can be seen as a specific case of the Blind Descent algorithm. We also train two neural network architectures, a multilayer perceptron and a convolutional neural network, using the most general Blind Descent algorithm to demonstrate a proof of concept.
